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OpenAI Software / SaaS Global

JetBrains

Application Refactoring · Enterprise Knowledge Search

JetBrains wanted to move beyond heuristic code-intelligence features and embed generative AI inside the IDE so developers could write, refactor, understand, and troubleshoot code without leaving their workflow.

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55% report having more time for more engaging tasks
71% report completing tasks more quickly
77% of polled developers report feeling more productive
78% report spending less time searching for information

Reported impact

Business outcome

JetBrains launched the fastest-growing product in its history, improved developer productivity and task speed, and created a path for increasingly context-aware AI-native development experiences inside its IDE portfolio.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

JetBrains integrated the OpenAI API into its AI Assistant product so developers can bring natural-language reasoning directly into familiar IDE workflows. Building on years of code completion, inspection, and context-aware heuristics, the company used OpenAI to accelerate a bigger leap: offloading routine work such as writing tests, naming variables, generating documentation, creating commit messages, explaining errors, suggesting fixes, and refactoring code snippets. Because the assistant runs inside IDEs like IntelliJ IDEA and PyCharm, developers avoid the switching costs of jumping out to browsers or separate tools, and JetBrains can pair model responses with rich project context from its code-insight systems. The company is now extending that foundation toward text-prompted project-structure generation, terminal-task automation, and even tighter context injection so LLMs can reason about a project the way the IDE already does.

Data flow

Developer prompts and editor context are sent from JetBrains IDEs into AI Assistant, which uses the OpenAI API to generate code suggestions, explanations, documentation, and fixes. The outputs stay inside the IDE workflow, where users can apply them without leaving their project context.

Solution architecture

2 components · 2 layers
  1. Compute
    • OpenAI API Provides the reasoning and generation capabilities behind JetBrains AI Assistant.
  2. Serving
    • AI Assistant Delivers inline coding, refactoring, explanation, and documentation features directly inside JetBrains IDEs.

Architecture clues

  • AI Assistant runs inside the IDE rather than requiring a browser
  • JetBrains chose OpenAI first after comparing latency, accuracy, throughput, and reasoning quality across providers
  • JetBrains wants to feed IDE code-insight context into future LLM-driven features
  • Planned features include project structure generation and terminal-task automation

Evidence from the source

"77% of polled developers report feeling more productive"
"Developers can offload mundane and less creative tasks, like writing tests and naming variables, to the AI Assistant."
"JetBrains AI Assistant operates inside the IDE"
"JetBrains incorporated OpenAI’s API into its AI Assistant product."
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. OpenAI

    JetBrains customer story

    First-party
    https://openai.com/index/jetbrains/
    Source type
    Vendor-published customer story
    Published
    Mar 18, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Mar 18, 2024 · OpenAI · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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